BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes

Karadeniz, İlknur; Tuna, Ömer Faruk; Özgür, Arzucan

doi:10.18653/v1/d19-5722

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…The BLAIR GMU team [42] used a machine learning and embedding-based method. The BOUN-ISIK team [43] used a static word embedding-based method integrating syntactic information. The AmritaCen Healthcare team also participated but did not publish the details of their method.…”

Section: Evaluation Strategymentioning

confidence: 99%

C-Norm: a neural approach to few-shot entity normalization

et al. 2020

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Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics. Results Our approach greatly outperforms all methods evaluated on the Bacteria Biotope datasets of BioNLP Open Shared Tasks 2019, without integrating any manually-designed domain-specific rules. Conclusions Our results show that relatively shallow neural network methods can perform well in domains that present highly multi-class and few-shot learning problems.

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Section: Evaluation Strategymentioning

confidence: 99%

C-Norm: a neural approach to few-shot entity normalization

et al. 2020

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An analysis of entity normalization evaluation biases in specialized domains

Ferré

Langlais

2023

BMC Bioinformatics

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Background Entity normalization is an important information extraction task which has recently gained attention, particularly in the clinical/biomedical and life science domains. On several datasets, state-of-the-art methods perform rather well on popular benchmarks. Yet, we argue that the task is far from resolved. Results We have selected two gold standard corpora and two state-of-the-art methods to highlight some evaluation biases. We present non-exhaustive initial findings on the existence of evaluation problems of the entity normalization task. Conclusions Our analysis suggests better evaluation practices to support the methodological research in this field.

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AcX

et al. 2022

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In this information-accumulating world, each of us must learn continuously. To participate in a new field, or even a sub-field, one must be aware of the terminology including the acronyms that specialists know so well, but newcomers do not. Building on state-of-the art acronym tools, our end-to-end acronym expander system called AcX takes a document, identifies its acronyms, and suggests expansions that are either found in the document or appropriate given the subject matter of the document. As far as we know, AcX is the first open source and extensible system for acronym expansion that allows mixing and matching of different inference modules. As of now, AcX works for English, French, and Portuguese with other languages in progress. This paper describes the design and implementation of AcX, proposes three new acronym expansion benchmarks , compares state-of-the-art techniques on them, and proposes ensemble techniques that improve on any single technique. Finally, the paper evaluates the performance of AcX and related work MadDog system in end-to-end experiments on a new human-annotated dataset of Wikipedia documents. Our experiments show that AcX outperforms MadDog but that human performance is still substantially better than the best automated approaches. Thus, achieving Acronym Expansion at a human level is still a rich and open challenge.

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BOUN-ISIK Participation: An Unsupervised Approach for the Named Entity Normalization and Relation Extraction of Bacteria Biotopes

Cited by 4 publications

References 24 publications

C-Norm: a neural approach to few-shot entity normalization

C-Norm: a neural approach to few-shot entity normalization

An analysis of entity normalization evaluation biases in specialized domains

AcX

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